{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#  Experimenting with Zorder and overlapping Axes\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = [0,1,2,3,4]\n",
    "yR = [2,4,3,5,3.5]\n",
    "yY = [4850,4400,4800,4800,5000]\n",
    "\n",
    "x2 = [0,1,1.7,2.4,4]\n",
    "yG = [4500,4500,4500,4950,4950]\n",
    "\n",
    "yW = [4700,4720,4730,4740,4750]\n",
    "\n",
    "axpos = [0.1,0.1,0.8,0.8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red    zorder= 0  visible= True  facecolor= (0.0, 0.0, 0.0, 1)\n",
      "Yellow zorder= 0  visible= True  facecolor= (1.0, 1.0, 1.0, 0.05)\n",
      "Green  zorder= 0  visible= False  facecolor= (1.0, 1.0, 1.0, 1.0)\n",
      "White  zorder= 0  visible= False  facecolor= (1.0, 1.0, 1.0, 1.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,5))\n",
    "\n",
    "##fig.patch.set_alpha(0.0)\n",
    "axR = fig.add_axes( axpos )\n",
    "axR.set_facecolor('k')\n",
    "\n",
    "axY = fig.add_axes( axpos, sharex=axR )\n",
    "axY.yaxis.tick_right()\n",
    "axY.yaxis.set_label_position(\"right\")\n",
    "#axY.patch.set_visible(False)\n",
    "axY.patch.set_alpha(0.05)\n",
    "\n",
    "axG = axY.twiny()\n",
    "axW = axG.twiny()\n",
    "\n",
    "#ax1.patch.set_visible(False)\n",
    "#ax2.patch.set_visible(False)\n",
    "\n",
    "#axR.set_facecolor('k')\n",
    "axR.patch.set_facecolor('k')\n",
    "#axY.patch.set_facecolor('k')\n",
    "#axG.patch.set_facecolor('k')\n",
    "#axW.patch.set_facecolor('k')\n",
    "\n",
    "#axY.patch.set_visible(True)\n",
    "#axG.patch.set_visible(True)\n",
    "#axW.patch.set_visible(True)\n",
    "\n",
    "_ = axR.plot(x1, yR, linewidth=15, color='magenta')\n",
    "_ = axY.plot(x1, yY, linewidth=15, color='yellow')\n",
    "_ = axG.plot(x2, yG, linewidth=15, color='lime')\n",
    "_ = axW.plot(x2, yW, linewidth=15, color='white')\n",
    "\n",
    "def print_zorders():\n",
    "    print('Red    zorder=',axR.get_zorder(),' visible=',axR.patch.get_visible(),' facecolor=',axR.patch.get_facecolor())\n",
    "    print('Yellow zorder=',axY.get_zorder(),' visible=',axY.patch.get_visible(),' facecolor=',axY.patch.get_facecolor())\n",
    "    print('Green  zorder=',axG.get_zorder(),' visible=',axG.patch.get_visible(),' facecolor=',axG.patch.get_facecolor())\n",
    "    print('White  zorder=',axW.get_zorder(),' visible=',axW.patch.get_visible(),' facecolor=',axW.patch.get_facecolor())\n",
    "    \n",
    "print_zorders()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-5-a96ba3aab008>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-a96ba3aab008>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    stop here\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "stop here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# axes = [axR,axY,axG,axW]\n",
    "# for ax in axes:\n",
    "#     ax.patch.set_facecolor('lime')\n",
    "#     print(ax.get_facecolor(),ax.patch.get_facecolor())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Apparently `ax.facecolor` and `ax.patch.facecolor` ARE ONE AND THE SAME, but**\n",
    "- **`ax.alpha` and `ax.patch.alpha` ARE DIFFERENT, and**\n",
    "- **`ax.visible` does _NOT_ exist but `ax.patch.visible` _DOES_ exist.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# axes = [axR,axY,axG,axW]\n",
    "# for ax in axes:\n",
    "#     ax.patch.set_alpha(0.1)\n",
    "#     print(ax.get_alpha(),ax.patch.get_alpha())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axR.set_zorder(0.32)\n",
    "axY.set_zorder(0.31)\n",
    "axG.set_zorder(0.32)\n",
    "axW.set_zorder(0.30)\n",
    "print_zorders()\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axR.set_zorder(0.32)\n",
    "axY.set_zorder(0.31)\n",
    "axG.set_zorder(0.32)\n",
    "axW.set_zorder(0.30)\n",
    "axG.patch.set_visible(False)\n",
    "axR.patch.set_visible(False)\n",
    "print_zorders()\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "STOP HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axR.set_zorder(0.3)\n",
    "axY.set_zorder(0.4)\n",
    "axG.set_zorder(0.5)\n",
    "axW.set_zorder(0.29)\n",
    "#axY.patch.set_visible(True)\n",
    "#axG.patch.set_visible(True)\n",
    "#axR.patch.set_visible(False)\n",
    "print_zorders()\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axR.set_zorder(0.1)\n",
    "axY.set_zorder(0.2)\n",
    "axG.set_zorder(0.3)\n",
    "axW.set_zorder(0.05)\n",
    "axR.patch.set_visible(True)\n",
    "print_zorders()\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apparent Rules for Overlapping Axes:\n",
    "\n",
    "1. Items drawn first appear *underneath* items drawn later.\n",
    "2. There is a zorder for each plot element, and a separate zorder for each Axes.\n",
    "3. Each Axes is drawn completely, with its plotted data, before moving on to draw the next Axes.  In other words, drawing is done per Axes at a time. If you have overlapping Axes, all elements of the second Axes are drawn on top of the first Axes, irrespective of the elements relative zorder.\n",
    "4. Axes are drawn according to the zorder *of the Axes*.\n",
    "5. If two or more Axes have the same zorder value, then Axes are drawn in the order they were created (i.e. the order that they appear in the `list` `Figure.axes`).\n",
    "\n",
    "#### Still trying to verify the following:\n",
    "6. The first Axes created within a given Figure, us unique in that *it will obscure the data from other axes* if it is drawn after them.  \n",
    "7. This problem can be solved if the other Axes have `patch.set_visible(False)` ???\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axatt = dir(axR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "axlist = fig.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "pp = pprint.PrettyPrinter(indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# dirlist = dir(axlist[0])\n",
    "# #dirlist\n",
    "# for item in dirlist[0:]:\n",
    "#     obj0 = 'axlist[0].'+item\n",
    "#     obj1 = 'axlist[1].'+item\n",
    "#     #print(item+':\\n','   ',obj0==obj1,'\\n   ',eval(obj0),'\\n   ',eval(obj1))\n",
    "#     print(item+':')\n",
    "# #    pp.pprint(eval(obj0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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